How do I install the Product Analytics skill?

Run npx skillmds@latest add ashutoshsrivastava17/product-analytics in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Product Analytics safe to use?

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Which AI agents work with Product Analytics?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Product Analytics free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Product Analytics?

ashutoshsrivastava17 (@ashutoshsrivastava17) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Product Analytics by ashutoshsrivastava17

Name: Product Analytics
Author: ashutoshsrivastava17

Product Analytics

Design product analytics — event taxonomy, tracking plan, metric definitions, dashboard requirements, and insight workflows. TRIGGER when: user says /product-analytics or asks about product instrumentation and usage tracking.

File contents

Product Analytics Design

Process

Step 1: Define Analytics Goals

Question	Analytics Needed
Are users activating?	Activation funnel events
Are users engaged?	Session, feature usage events
Are users retaining?	Return frequency, churn events
Are users expanding?	Upsell, feature adoption events
Where do users struggle?	Error, rage-click, drop-off events

Step 2: Design Event Taxonomy

Convention	Format	Example
Object_Action	noun_verb (past tense)	`project_created`, `report_exported`
Properties	snake_case, consistent types	`plan_type: "pro"`, `item_count: 5`
Naming rules	No abbreviations, consistent casing	`subscription_upgraded` not `sub_up`

Step 3: Create Tracking Plan

Event	Trigger	Properties	Priority
page_viewed	Page load	page_name, referrer	Must
button_clicked	CTA click	button_name, location	Must
feature_used	Feature activation	feature_name, duration	Must
error_occurred	Error displayed	error_type, error_message	Must
search_performed	Search submitted	query, results_count	Should

Step 4: Define Metrics

Metric	Definition	Formula
DAU/MAU	Daily/Monthly active users	Unique users with session
Activation rate	Users completing setup	Activated / Signed up
Feature adoption	Users using feature X	Feature users / MAU
Retention (D7)	Return after 7 days	Active D7 / Cohort size

Step 5: Design Dashboards

Dashboard	Audience	Metrics
Executive	Leadership	MAU, retention, revenue
Product	PM/Design	Feature adoption, funnels
Growth	Marketing	Acquisition, activation
Engineering	Dev team	Errors, performance

Step 6: Establish Insight Workflow

Weekly metrics review (automated report)
Anomaly investigation (dig into unusual changes)
Hypothesis formation (why did metric change?)
Experiment design (A/B test to validate)
Decision and iteration

Output Format

## Analytics Plan: [Product]
### Event Taxonomy: [Naming convention and key events]
### Tracking Plan: [Event table]
### Metrics: [Definitions and targets]
### Dashboards: [Requirements by audience]

Quality Checklist

Event naming is consistent and documented
All key user actions have tracking events
Metrics have clear definitions and formulas
Dashboard requirements match stakeholder needs
Privacy and consent handled properly

Edge Cases

For mobile apps, track offline events with batched upload
For multi-platform, ensure consistent event names across platforms
If GDPR applies, implement consent management before tracking

---name: product-analyticsdescription: Design product analytics — event taxonomy, tracking plan, metric definitions, dashboard requirements, and insight workflows. TRIGGER when: user says /product-analytics or asks about product instrumentation and usage tracking.---# Product Analytics Design## Process### Step 1: Define Analytics Goals| Question | Analytics Needed ||----------|-----------------|| Are users activating? | Activation funnel events || Are users engaged? | Session, feature usage events || Are users retaining? | Return frequency, churn events || Are users expanding? | Upsell, feature adoption events || Where do users struggle? | Error, rage-click, drop-off events |### Step 2: Design Event Taxonomy| Convention | Format | Example ||-----------|--------|---------|| Object_Action | noun_verb (past tense) | `project_created`, `report_exported` || Properties | snake_case, consistent types | `plan_type: "pro"`, `item_count: 5` || Naming rules | No abbreviations, consistent casing | `subscription_upgraded` not `sub_up` |### Step 3: Create Tracking Plan| Event | Trigger | Properties | Priority ||-------|---------|-----------|----------|| page_viewed | Page load | page_name, referrer | Must || button_clicked | CTA click | button_name, location | Must || feature_used | Feature activation | feature_name, duration | Must || error_occurred | Error displayed | error_type, error_message | Must || search_performed | Search submitted | query, results_count | Should |### Step 4: Define Metrics| Metric | Definition | Formula ||--------|-----------|---------|| DAU/MAU | Daily/Monthly active users | Unique users with session | | Activation rate | Users completing setup | Activated / Signed up || Feature adoption | Users using feature X | Feature users / MAU || Retention (D7) | Return after 7 days | Active D7 / Cohort size |### Step 5: Design Dashboards| Dashboard | Audience | Metrics ||-----------|----------|---------|| Executive | Leadership | MAU, retention, revenue || Product | PM/Design | Feature adoption, funnels || Growth | Marketing | Acquisition, activation || Engineering | Dev team | Errors, performance |### Step 6: Establish Insight Workflow1. Weekly metrics review (automated report)2. Anomaly investigation (dig into unusual changes)3. Hypothesis formation (why did metric change?)4. Experiment design (A/B test to validate)5. Decision and iteration## Output Format```markdown## Analytics Plan: [Product]### Event Taxonomy: [Naming convention and key events]### Tracking Plan: [Event table]### Metrics: [Definitions and targets]### Dashboards: [Requirements by audience]```## Quality Checklist- [ ] Event naming is consistent and documented- [ ] All key user actions have tracking events- [ ] Metrics have clear definitions and formulas- [ ] Dashboard requirements match stakeholder needs- [ ] Privacy and consent handled properly## Edge Cases- For mobile apps, track offline events with batched upload- For multi-platform, ensure consistent event names across platforms- If GDPR applies, implement consent management before tracking

Frequently asked questions